strreplace#

Purpose#

Replace all matches of a substring with a replacement string.

Format#

str_new = strreplace(str, search, replace)#

Parameters:

str (string, string array, or categorical variable of a dataframe) – to be searched and modified.
search (string) – the substring to search for in str.
replace (string) – the substring with which to replace all instances of search found in str.

Returns:

str_new (string) – new string, string array of categorical variable of a dataframe which is the same as str, except that all instances of search have been replaced with replace.

Examples#

Basic search and replace#

// String to be searched in
str = "My doctor recommends more chocolates, because chocolates are healthy.";

// String to be searched for
search = "chocolate";

// String to be replaced with
replace = "vegetable";

// Build new string
new_str = strreplace(str, search, replace);

After the code above, new_str will be set to:

My doctor recommends more vegetables, because vegetables are healthy.

Regularize addresses in string array#

// String array to be searched
str = "100 Main Ave" $|
      "112 Charles Avenue" $|
      "49 W State St" $|
      "24 Third Avenue";

// String to search for
search = "Avenue";

// String to replace with
replace = "Ave";

// Build new string
new_str = strreplace(str, search, replace);

After the code above, new_str will be set to:

   "100 Main Ave"
"112 Charles Ave"
  "49 W State St"
   "24 Third Ave"

Change the name of categories in a dataframe#

// Create 5x1 string array
states = "CA" $| "FL" $| "California" $| "California" $| "FL";

// Convert the string array to a dataframe
// with the variable name 'States'
df_states = asdf(states, "States");

print df_states;

    States
        CA
        FL
California
California
        FL

When asdf() created the dataframe df_states from the string array, states, it made a categorical variable and assigned a separate category to each different string it found in states.

We can use the getcollabels() function to show the categories and labels found.

// Get the category labels and keys from the
// first (and only) variable in 'df_states'
{ label, keys } = getcollabels(df_states, 1);

After the above code:

labels = "CA"             keys = 0
         "California"            1
         "FL"                    2

// Replace the "California" label with "CA"
// and remove the "California" category
df_states = strreplace(df_states, "California", "CA");

print df_states;

States
    CA
    FL
    CA
    CA
    FL

// Get the new category labels and keys
{ label, keys } = getcollabels(df_states, 1);

As we see below, the observations that previously had the label "California" and a key value of 1, have now been merged with the "CA" category.

labels = "CA"             keys = 0
         "FL"                    2